Functional traits
This article should be read in conjunction with the articles Functional groups, Biological Trait Analysis and Biodiversity, ecosystem functioning and ecosystem function
Definition of Biological trait / Trait:
Any measurable characteristic of an individual organism, such as its shape (morphological), how it functions (physiological), or the timing of its life events (phenological). [1].
This is the common definition for Biological trait / Trait, other definitions can be discussed in the article
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The term 'biological trait' is often used broadly to include all measurable organism characteristics, of which functional traits are a subset.
Definition of Functional trait:
Functional traits are measurable characteristics of organisms that influence their performance, their responses to environmental conditions, or their effects on ecosystem processes. [2][1]
This is the common definition for Functional trait, other definitions can be discussed in the article
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Lavorel and Garnier (2002[2]) distinguished 'response traits', which are associated with organism responses to environmental factors such as resources and disturbances, from 'effect traits', which determine effects on ecosystem processes. Violle et al. (2007[1]) more specifically related functional traits to individual performance through their effects on growth, reproduction and survival.
Trait values assigned to species are generally useful approximations, but some traits vary among individuals, life stages and environmental conditions.[3]
Contents
Functional Diversity
Functional diversity describes how different the organisms in a community are in ecologically relevant traits, rather than simply how many species are present. It is often used as a trait-based measure of biodiversity. Functional diversity can be quantified in multiple ways, including measures such as functional richness, evenness, and divergence (see Measurements of biodiversity). It focuses on their ecological roles, rather than simply who they are.
Functional traits are biological traits selected because they are relevant to a particular ecological question. They may determine how organisms respond to environmental conditions or disturbance, how they affect ecosystem processes, or both[4] and can be used as indicators to infer ecosystem functioning.
Examples of phytoplankton functional traits include: cell size and shape, motility, nutrient requirements, nitrogen-fixation capacity, silica requirement and tolerance of temperature or salinity. Depending on the ecological question, some of these traits can act as response traits, effect traits, or both. For example, nutrient requirements influence which environmental conditions favor a species, whereas nitrogen fixation can alter nutrient availability for the wider ecosystem.
Species with similar functional traits are often classified into functional groups. Because traits generally vary continuously among species, functional groups simplify this continuous variation by grouping species with sufficiently similar traits for the ecological question being considered.
Why Trait-Based Approaches Matter
Studying traits helps ecologists understand:
- why particular species are favored under particular environmental conditions;
- how community composition changes under disturbance or environmental change;
- how changes in community composition can affect ecosystem processes.
Trait-based approaches can help explain ecological strategies and community assembly. Trait differences may contribute to niche differentiation, although functional traits alone generally do not predict whether species will coexist.[5][6].
Trait-based approaches complement taxonomy. A species list can show that a community has changed, whereas traits can help explain whether the change reflects greater sensitivity to disturbance, slower recovery, altered feeding, reduced sediment mixing or another ecological mechanism. Because similar traits occur in unrelated species, trait approaches can also reveal common responses across different regions and communities.
Response traits such as lifespan, body size and mobility can help explain why species differ in sensitivity and recovery under environmental pressures or disturbance, for example:
Biological Trait Analysis (BTA)
Biological Trait Analysis (BTA) uses information on selected biological traits to describe the functional composition of ecological communities. It does not measure ecosystem functioning directly, but trait composition can be used to infer potential ecosystem processes and responses to environmental pressures. However, choosing which traits to include is crucial; many traits are available, but not all are equally useful. The choice also depends on available data and the time and cost of analysis[11]
The choice of traits determines what a trait analysis means. Traits selected to assess vulnerability to disturbance need not be the traits that best describe ecosystem processes. For example, bottom trawling may preferentially remove large, long-lived and slowly reproducing benthic organisms (response traits). If these organisms are also deep burrowers or strong sediment reworkers (effect traits), their loss can reduce sediment mixing and nutrient exchange[12]. This example illustrates that traits should be selected according to the ecological question, rather than simply because data are available.[13]
Related articles
- Functional groups
- Biological Trait Analysis
- Functional diversity
- Measurements of biodiversity
- Marine Biodiversity
References
- ↑ 1.0 1.1 1.2 Violle, C., Navas, M-L., Vile, D., Kazakou, E., Fortunel, V., Hummel, I. and Garnier, E. 2007. Let the concept of trait be functional! Oikos 116: 882–892
- ↑ 2.0 2.1 Lavorel, S. and Garnier, E. 2002. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Func. Ecol. 16: 545–556
- ↑ De Juan, S., Bremner, J., Hewitt, J., Törnroos, A., Mangano, M.C., Thrush, S. et al. 2022. Biological traits approaches in benthic marine ecology: Dead ends and new paths. Ecology and Evolution 12: e9001.
- ↑ Diaz, S. and Cabido, M. 2001. Vive la difference: plant functional diversity matters to ecosystem processes. Trends in Ecology and Evolution 16: 646-655
- ↑ Reiss, J., Bridle, J.R., Montoya, J.M. and Woodward, G. 2009. Emerging horizons in biodiversity and ecosystem functioning research. Trends Ecol. Evol. 24: 505-514
- ↑ Grime, J.P. 2006. Trait convergence and trait divergence in herbaceous plant communities: Mechanisms and consequences. J. Vegetation Science 17: 255-260
- ↑ Hu, C., Dong, J., Gao, L., Yang, X., Wang, Z. and Zhang, X. 2019. Macrobenthos functional trait responses to heavy metal pollution gradients in a temperate lagoon. Environmental Pollution 253: 1107–1116
- ↑ van Denderen, P.D., Bolam, S.G., Friedland, R., Hiddink, J.G., Norén, K., Rijnsdorp, A.D., Sköld, M., Törnroos, A., Virtanen, E.A. and Valanko, S. 2020. Evaluating impacts of bottom trawling and hypoxia on benthic communities at the local, habitat, and regional scale using a modelling approach. ICES Journal of Marine Science 77: 278–289
- ↑ Kendzierska, H. and Janas, U. 2024. Functional diversity of macrozoobenthos under adverse oxygen conditions in the southern Baltic Sea. Scientific Reports 14: 8946.
- ↑ Hiddink, J.G., Jennings, S., Sciberras, M., Bolam, S.G., Cambiè, G., McConnaughey, R.A., Mazor, T., Hilborn, R., Collie, J.S., Pitcher, C.R., Parma, A.M., Suuronen, P., Kaiser, M.J. and Rijnsdorp, A.D. 2019. Assessing bottom-trawling impacts based on the longevity of benthic invertebrates. Journal of Applied Ecology 56: 1075–1084
- ↑ Gayraud, S., Statzner, B., Bady, P. and Haybach, A. 2003. Invertebrate traits for the biomonitoring of large European rivers: An initial assessment of alternative metrics. Freshwater Biology 48: 2045 – 2064
- ↑ Hinz, H., Törnroos, A. and de Juan, S. 2021. Trait-based indices to assess benthic vulnerability to trawling and model potential loss of ecosystem functions. Ecological Indicators 122: 107267
- ↑ Bremner, J., Rogers, S.I. and Frid, C.L.J. 2006. Matching biological traits to environmental conditions in marine benthic ecosystems. J. Mar. Syst. 60: 302–316
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